Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
The electrocardiogram (ECG) may be an important physiological signal for cardiac diagnosis. Typically, an ECG signal includes a large quantity of data that increases with sampling rate, sample resolution, etc. For example, storing a twenty-four hour recording of ECG signal data for subsequent analysis may require on the order of 100 megabytes of digital storage. Hence, ECG data compression may be important for storage, transmission and telediagnosis.
ECG compression aims to reduce the amount of digitized ECG data while preserving important diagnosis features of the ECG signal. Various ECG data compression methods have been proposed over the last three decades. These approaches can be divided into two major categories: direct methods and transform methods. Known direct data compression methods include the amplitude zone epoch coding (AZTEC) technique, the turning point (TP) algorithm, the scan-along polygonal approximation (SAPA), to name a few examples. In transform coding methods, a linear transform may be applied to an ECG signal before applying compression in the transform domain. Known transform techniques include Fourier transform, Walsh transform, discrete cosine transform (DCT) and wavelet transform (WT), to name a few examples. While, in general, direct methods may be better than the transform methods when considering the complexity of the compression system, transform methods often provide improved compression ratios and may not be as sensitive to noise in the ECG data.